A commodity is an interchangeable good or material, bought and sold freely of similar value as an article of commerce. It comes in four main categories, including agricultural products, energy, metal, and livestock and meat. Dated back thousands of years ago, commodities pricing and trading have always played a vital role, both economically and politically, in the rise of many empires. Over the years, commodity markets have grown along with the constant development of products. Nowadays, modern commodities are traded on exchanges, such as the London Metal Exchange (LME), the Chicago Mercantile Exchange (CME), New York Mercantile Exchange (NYMEX), etc.
The main purpose of commodity exchanges is to create a marketplace for producers to sell their commodities to those buyers in need. Having said that, it associates with extreme volatility caused by speculators, who purchase assets for a short time with the expectation to profit from fluctuation in its price. Consequently, they might never perform the actual delivery of the commodity itself.
Artificial Intelligence (AI) and Machine Learning (ML) have emerged as the most common tech buzzwords yet most commonly confused with each other. In the simplest definition, AI is the umbrella term describing the various tools and algorithms that enable machines to have human-like intelligence. And ML is one of AI’s tools that allow computer programs to automatically improve through experience in order to make better decisions. Unlike traditional algorithms which tend to follow explicit instructions to execute a specific task, Machine Learning incorporates various contextual variables and their relationship during training. ML comes in three types: Supervised Learning, Unsupervised Learning and Reinforcement Learning. Price Forecasting mainly involves Supervised Learning algorithms, which in turn applies to Time Series Data.
Many studies have been conducted in order to enhance the predictability of Time-Series Data for different applications aside from finance. Examples of time series applications are patient health evolution metrics, monthly rainfall, heights of ocean tides, traffic jams, etc. Algorithms which have gained popularity in the domain of time series forecasting are Gaussian Processes, Random Forests, and Long Short-Term Memory Networks (LSTMs).
There is still mighty ambiguity regarding the comparison between Machine Learning and classical methods. It is essential to take in to account several factors such as error metrics, underlying data, and other adjustive parameters. As a result, we compare various methods in the context of Time Series Data and evaluate different models based on thoughtful analysis.
Forecasty.AI’s goal is to provide the most accurate forecasts for commodities prices using the latest AI techniques. We focus on forecasting methods that are simple and adaptable to time series data, that are easy interpreted and understood by decision-makers. Indeed, we use statistical AI models that only require minimal inputs to generate a precise forecasting model. These simple approaches work best in real-world applications as they lessen the chances of data mining problems. We also apply various ML techniques including Random Forest, XGBoost, Support Vector Machines and Gaussian Processes. This aids in the understanding of key drivers of the commodity markets that enable smarter investments.
Forecasty.AI runs on advanced ML-powered forecasting models that provide a highly accurate forecast into the future prices of the commodities. We have currently collaborated with global clients across various industries and are looking forward to expanding further beyond.
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